Harnessing brassinosteroid signaling in rice: from molecular pathways to environmentally adaptive breeding

Brassinosteroids (BRs), a class of steroidal phytohormones, play crucial roles in regulating plant growth, development, and environmental adaptation in rice. With the elucidation of BR signaling pathways in <i>Arabidopsis</i>, the rice BR regulatory network has been progressively uncovered, revealing both conserved and unique features. In this review, we first summarize recent advances in understanding BR signaling mechanisms in rice. We then focus on how BR signaling integrates environmental cues, including nutrient availability, high-temperature stress, drought stress, and pathogen responses, to fine-tune growth and yield. Moreover, we highlight the complex cross-talk between BR signaling and other phytohormones that enables dynamic responses to environmental fluctuations. Finally, we discuss the potential applications of BR-related genes in rice production, together with the challenges of their translation into practical agricultural systems, providing perspectives and opportunities for environmentally adaptive breeding. Taken together, recent discoveries deepen our understanding of BR signaling in rice and provide a conceptual framework for exploring its roles in environmentally adaptive growth regulation and crop improvement.

Authors

Institutions

Publication Details

Journal
Frontiers in Plant Science
Published
2026-02-25
DOI
https://doi.org/10.3389/fpls.2026.1781641
Primary Topic
Plant Molecular Biology Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Harnessing brassinosteroid signaling in rice: from molecular pathways to environmentally adaptive breeding

Lilin Men, Zhonghua Sun, Shupeng Xie, Z. M. Cheng et al.
Frontiers in Plant Science
Plant Molecular Biology Research
article

Harnessing brassinosteroid signaling in rice: from molecular pathways to environmentally adaptive breeding

Lilin Men, Zhonghua Sun, Shupeng Xie, Z. M. Cheng, Jinhui Zhang, Hanjing Sha, Tianpeng Zong, Shuai Li, LiChao Liu, ZhongHua Wei
article en

Abstract

Brassinosteroids (BRs), a class of steroidal phytohormones, play crucial roles in regulating plant growth, development, and environmental adaptation in rice. With the elucidation of BR signaling pathways in <i>Arabidopsis</i>, the rice BR regulatory network has been progressively uncovered, revealing both conserved and unique features. In this review, we first summarize recent advances in understanding BR signaling mechanisms in rice. We then focus on how BR signaling integrates environmental cues, including nutrient availability, high-temperature stress, drought stress, and pathogen responses, to fine-tune growth and yield. Moreover, we highlight the complex cross-talk between BR signaling and other phytohormones that enables dynamic responses to environmental fluctuations. Finally, we discuss the potential applications of BR-related genes in rice production, together with the challenges of their translation into practical agricultural systems, providing perspectives and opportunities for environmentally adaptive breeding. Taken together, recent discoveries deepen our understanding of BR signaling in rice and provide a conceptual framework for exploring its roles in environmentally adaptive growth regulation and crop improvement.

Frontiers in Plant ScienceVol. 17
Heilongjiang Provincial Academy of Agricultural Sciences (CN), Chinese Academy of Agricultural Sciences (CN), Institute of Crop Sciences (CN)
Zero hunger
Openalex Percentile: Top 44%
Plant Molecular Biology Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.